Coral reef texture classification using support vector machines

The development of tools to examine the ecological parameters of coral reefs is seriously lagging behind available computer-based technology. Until recently the use of images in environmental and ecological data gathering has been limited to terrestrial analysis because of difficulties in underwater image capture and data analysis. In this paper, we propose the application of computer vision to address the problem of monitoring and classifying coral reef colonies. More specifically, we present a method to classify coral reef images based on their textural appearance using support vector machines (SVM). Our algorithm uses raw pixel color values directly as sample vectors. We show promising results on region classification of three coral types for low quality underwater images. This will allow for more timely analysis of coral reef images and broaden the capabilities of underwater data interpretation.